In this episode George Firican, Director of Data Governance and BI at the University of British Columbia and founder of LightsOnData talks about how his team uses data to help fundraise for the university. Listen to the episode to learn some of George’s favorite resources for budding data scientists and tips for growing your career.
(upbeat music) - This is "Buffalo State Data Talk," the podcast where we introduce
you to how data is used and explore careers that involve data. - Hello and welcome
back to another episode
of "Buffalo State Data Talk." I'm your host, Heather Campbell, and thank you for joining us. Today, we talking to George Firican, the Director of Data Governance
And Business Intelligence
at the University of British Columbia and founder of Lights On Data. Welcome to the show, George. - Thank you Heather, thank you so much for having me.
It's such a pleasure to be here. - So could you start us off by telling us a bit about what you do as a director of data governance
and business intelligence? - Absolutely, so let me just
start by kind of breaking down
what the department is all about. Overall, it really just provides that data reporting and analytics support to the engagement and the
fundraising initiatives across our university.
And actually it's comprised
of four different units or four different functions. So the first two, you
might have guessed there, well one of them is data governance, the other one is business intelligence.
Within data governance, we just try to ensure that
those important data assets are formally managed
throughout university. Within the business intelligence, again, we try to provide that accessible,
easily understood information
through dashboards, through data polls, reports
and different audits. And of course, we kind of also
lead the data integrations, data maintenance efforts. And the other two,
which are not in the department title, but they are there in their
really core functions. One of them is records, and records are mostly
doing the data entry, they're maintaining and
updating biographical
and other constituent
information within our main CRM. And then we also have the data analytics, which is overseeing all
those analytics initiatives across the university when it comes to the engagement
and the fundraising world.
So it involves modeling and
segmentation and forecasting and all the fun stuff. And my role is more on the strategy piece, but also quite a bit of
hands on the data governance. It's data governance from,
I guess the lead is kind
of just the one person, but I work with business analysts and a lot of those business stakeholders to try and put together
the business glossary so that refers to the business definitions
that we have on business
terms and their meanings and how they tie back to the data and then how that data should
be best used and integrated and just disseminated across our users. - So what does a typical week look like
for you in the position? - You know, I would have
to say that maybe 80% of it is spent in meetings. A lot of meetings, I think maybe a half an hour to two a day
where I could actually catch
up on work and do some stuff, but a lot of it is
meeting with stakeholders, meeting with my colleagues
to try and figure out, okay, what type of projects
do we have on hand? What are the roadblocks
that we need to remove?
What are the risks that we can identify and then reduce that risk from happening, and, you know, convince stakeholders to get on board with certain things. So there's a lot of
presentation that are happening.
There's a lot of, you know, input that needs to be provided. A lot of meetings, a lot of conversations, a lot of discussions,
especially you know, when it comes to coming
up with a new policy, when it comes to writing that definition and kind of agreeing, you know, what's a student?
And it might sound easy because how could you not
know what a student is? I mean, you are a university, but there are just so
many different facets. So I think those conversations
can take months at times.
- I think you bring up
such an important point. Sometimes people I think kind
of overlook data governance and you know, the idea
of like, oh, a glossary, like, oh like defining a student, you're like, oh, that's simple,
of course it's a student,
a student is easy. No it's not easy, it takes
so much more than that. - And you know, I mean, companies are facing similar issues, but when it comes to customer,
that's really like the big piece that always needs to be defined. And again, it sounds
easy from the outside, but there are just so
many different intricacies and that's why internally
you usually have different
types of customers and you finally decide
on what customer overall as the organization mean. - You mentioned before that there's kind of a bunch of different areas
within this research
team that you work on. Could you tell me a little
bit about the different roles of the team members and the
background of these people? What kind of education
that they would need for these positions?
- Absolutely. Yeah, so on the business intelligence side and on the data analytics side, they're a little bit more technical, but we have what we call the
business intelligence analyst
or the business intelligence programmer. And they're really tasked making sure that when they have a data request
from one of the stakeholders that they're accessing
those different databases and pulling that data
in the way that it needs to be consumed, and that can be as simple as
pulling an Excel spreadsheet or building a report, or building a dashboard in order to provide that information
back to the stakeholders. They're also, you know, developing these tools such
as reports and dashboards, and even working in customizing the CRM in order to surface
all of this information
and provide these lists
to our stakeholders. And for that, well, again, they are highly technical and I think the most needed
skill to have is SQL. Oh, and there's, let's not
forget about the data architect
and the role is kind to implement the BI technical architecture vision and make decisions regarding design and
maintenance of the data and the technical ecosystem.
So kind of think of it as
how different tables of data should kind of talk to each other. How do you implement a new system, a new data mart, a new data warehouse that's maybe consuming
data from different places
and then outputting for different needs, and especially if there's some sort of an API integration layer between two systems that need to talk and sync data, transfer data,
they're really the brain
child be behind this and they are detecting the
best way on doing that. So that's the BI team. Then we have the data analytics team, they're really in charge of leading
those qualitative and
quantitative research to support a planning,
leadership, decision making, and you know, that comprehensive
program delivery strategy for our fundraising and
alumni engagement program. So they determine trends and
forecast growth opportunities.
You know, they provide
increased understanding in hypothesis testing of patterns and behavior for our
alumni, for our donors. They provide out the
statistical data analysis for planning discussions,
which include like regression analysis and perform data mining and integrate modeling
and engagement analysis. So a lot of maybe big words, but fun stuff as well
that kind of determine
for our main stakeholders which are alumni engagement
officers and fundraisers, who should they best
target with their message, with their reach, with their approach? - So you're talking about the data
that these people are
actually using and analyzing and looking at, what kind of data are they
actually looking at and using, and how are they using it? What are they doing with it?
- It it's a definitely a mix of things. So because of the nature
of the work that we do with the fundraising
alumni engagement teams, we're looking at a lot
of transactional data, all that gets tied to
what we call master data
and that goes back to the individual. So you're an alum, you're a
donor, you're a volunteer, you're a supporter, some sort of a friend of
the university per se, an event entity or whatnot.
So something that provides
you with an interaction with the university, as well as, you know,
organizations that you're part of, who do you work for? And then we can go bigger
into let's say big data,
which overlaps a little bit
with transactional data, because we're looking at, you know, what's your click through through the email that you've clicked on? Then we look at external data
that provides us with
different income levels at the neighborhood levels. So based on where you live, we're trying to then approximate what's your income and
what's your propensity,
what's your, you know, capacity for giving and for supporting and for volunteering back to the university? - So could you talk about a challenge that you or your team have had to overcome
and how did you solve it? - You know, there's so
many that come to mind and I think we're facing
challenges all the time. A lot of it is really, I might give you a couple of examples,
but I think a lot of 'em are really around the communication aspect and trying to convince
people and audiences to embark on a particular solution, which can take a lot of time.
So that's why change management practices are always in focus. We don't always follow the best practices as kind of human nature
takes its place instead, but you know, to best
create that engagement,
we're trying to make sure
that we contact the user so whatever we're gonna
deliver as soon as possible and then have 'em along the journey every step on the way. But to give you a couple of examples,
so something that we've delivered just in the past six months that we actually even
managed to win an award on was this project that we
called key market segmentation. So obviously our alumni
are all over the world
and we wanna make sure that
we identify those key regions where they are located so then we could formulate
different strategies on how to best engage with
them depending on the region. You know, what type of
events should we create?
What type of outreach should we have? Is it worth it to invest resources within a particular region versus another? Because we do have limited
resources as you know, any organization would,
so we need to be as efficient as possible. So the main challenge initially was, okay, well how are we going
to define those region? So think of it as, you know, New York. Well New York can mean different areas.
Are you talking about the state? Are you talking about the city? And if so within the city, is it like the greater New York or is this just specific
parts of New York?
Are you going to include Queens or not? So depending on who you would talk to, different definitions came up and there was a lot of inconsistencies and because of that,
we couldn't compare apples to apples. So that was the first hurdle to actually put some definitions on some of these main regions that we identified that we
have within our database.
That probably took quite
a few months to decide. So once we found these 21 regions, well now how do we program it so that we easily find the constituents, the alumni that fit within those regions.
And with that it was, you know,
a great challenge to undergo because we also wanted to have it as timely and updated as possible. So we had a mix of software
and tools that we used, including the Bing Maps engine,
which provided us the
coordinates on set address. And then we used GeoPy and Nominatim, which are kind of these Python libraries. Of course, Python was used as well for some of that automation,
PowerShell too and SQL server, and Tableau then to visualize everything and really physically see the regions with the areas on a map, visually striking
and then within the regions, we would actually see the
number of constituents and even be able to go
at that individual level and see, okay, oh, they live
on this corner of the street because the fun part then
would allow you to see,
you know what, where would be then a really
good event place for us to book that would be most convenient to most individuals within that region? So that was a lot of fun,
took a lot of months, it was a lot of trial
and error and research to find the best fit of all
these tools working together and then automating everything
so that it's seamless, behind the scenes, effective,
and yeah, it was such a great project to be able to be part of. - Yeah, that sounds amazing
and really, really interesting and hopefully really, really helpful for those people that
are planning the events.
That sounds awesome. - And not only, that's definitely one use
case and one important one, but not only. - Yeah, definitely.
I wanna talk a little bit
about your career path. So you have a Bachelor's
in Computer Science and a Masters in Business Management. So how did you end up working with data? How did you end up in your position?
- Great question. So after my computer science degree, I went and, you know,
used the skills I learned and I became a programmer
back and front end and I, you know, learned a
lot of things along the way,
worked for a couple of startups and worked on some interesting projects where I was able to wear different hats because there were usually
small teams as part of startups. And I recommend that to anybody,
if they have the opportunity
to go for a startup early in their a career, or even later, I think it's a great learning opportunity because you get to see different sides that otherwise in a big company
have a more scripted job description of where you kind of
just do the one thing. So because of that, as I was programming, obviously I worked on projects that have to do a lot with database.
So things like e-commerce for example, and I could see the impact of
how that data gets recorded to how that data gets then consumed. And as I was mentioning, I used to wear different hats
and one of the hats was to do the business
analysis part of it, and then I kind of stepped into the project management part of it and having more interaction
with the business
stakeholders, the clients. And as I was having that
interaction with the clients and learning more about their
requirements and their needs, and then how that needs to be translated into the how data gets
stored and displayed,
used, disseminated. There were a lot of aha
moments and a lot of, you know, things that I haven't thought of before. And that kind of made me think
a little bit more about data and the importance of data quality,
and managing that data properly. And I think it's often overlooked and it's sort of the
afterthought for most companies. So yeah, that made me
step into data quality, and you can't really do data quality
without data governance and here I am, you know, a lot of years later just focusing on data governance and how to best manage that data.
- So you mentioned your business, the Lights On Data
Consulting and Training, you are the founder of this. So could you tell us a
little bit about your company and what they do?
- Absolutely, so it's trying
to put the lights on data by educating people on
how to best manage it, how to best disseminating it. And right now the focus is
more on the data management, data governance,
but it's set to expand as well. So I do a lot of educational content in form of courses and articles, and templates and videos and whatnot. So some are free, some are paid.
But from my experience, it is what I didn't have when I started my journey
into data management, and data governance, and BI. So I tried to make it as
practical as possible.
I not only draw from my experience but those of other data professionals that I've worked with and interacted with and companies that I
worked for or worked with as I do and I did some
consulting on the side as well.
So a lot of experiences really put into it and making that information
as practical as possible and not so much theoretical. So it's something I'm hoping
you're going through a lesson and then that particular
knowledge that you've acquired,
you can apply it the next day at work, you can use a template that I provided to kind of get you
started and make it easier and just, yeah, make it fun and practical. - Yeah, that's exactly what
you need in data science.
You need to be able to have things that you can put to use right away. So if you're interested
in this information, how can you learn more
about Lights On Data? Where should people go?
- Well, please check out lightsondata.com, as easy as it sounds, or just Google it and check
out its YouTube channel or get in touch with me on
LinkedIn, George Firican, there's only two of us
and I am the younger one.
- And you are also the host of the "Lights On Data Show" podcast. So can you tell us what your podcast about and why should we tune in? - Oh, my pleasure.
So each one of the episode
kind of puts the lights on various data topics with really
renowned industry experts. So we cover a lot of topics, you know, in a fun and
informative interview format. So things on data science, and analytics,
and machine learning, AI, but also data visualization, data storytelling, data strategy, data management, data governance, and really so much more
kind of really caters to a wider audience. But that's a great thing that if you're only
interested in one piece, you can just follow those topics, but also think that as
a data professional,
it's really valuable to
have at least an awareness of everything that's data related, because it does impact
you as a data professional in one way or another. And it's also maybe a little bit different
about the podcast that I provide is, that it has that audience interaction. So we're filming a live and it goes on LinkedIn live, YouTube live, Twitter live. And through those channels,
if you manage to join in a live episode then you can have the
option to ask a question, provide feedback, put a comment that we often
bring back into the show and then we can address that question
and answer the question
with that industry expert that we're interviewing. - Oh how much fun and when
are your episodes live? - Usually every Friday
at 11:00 AM Eastern time, but you know, sometimes you can go
a little bit off schedule. So we had one earlier
this morning, for example. - Excellent. Well, we will put the
link to the Lights On Data and the "Lights On Data Show" podcast
in the description of this episode if anybody wants to check it out. - Thank you. - So you are also quite active on LinkedIn and you've used this to help
grow your professional network
and your personal brand. So could you maybe give
us some tips and tricks on how you've used LinkedIn for this? - Absolutely, you know what, if I can think of one
regret that I might have
is not getting on LinkedIn earlier, and let me rephrase that, not being active on LinkedIn earlier. I think a lot of professionals
have the impression that, you know what, LinkedIn is
just for posting your resume
and then when you apply for a job, maybe you can find a
job through it that way and just apply it. But it's so much more, it's a platform where
you can share experience
and learn from one another. And as soon as I found that and starting to share my knowledge back and even asking questions, asking for feedback,
asking for opinions from others, this brilliant new world really opened up and it really created a
lot of brand awareness for me as an individual, as well as my company.
So it was such a win-win situation. I formed a lot of great lasting
professional relationships and a lot of opportunities
came out because of it. So I think the lesson
there is to start sharing, start asking questions,
start engaging on LinkedIn with others. And I feel one of the
big roadblock is that if you're a student, if you're just early in your career, you're thinking, well you
know, I have nothing to share
and that's so not true. Even if you start sharing
your learning journey, I think that so many people can learn from that along the way too. So it's highly valuable for
you to start to be engaged
and put content out there
that's relevant to others. - And if you're not sure where to start, you can, you know, start
following other people and seeing what kinds of
things they're posting such as our guest George
and we will post a link to his LinkedIn in the description of this episode too. - Thank you. - So many of our listeners are younger. So as somebody in a leadership
role who has, you know,
mentored many people before, what advice would you have to give someone who's interested in
working as a data analyst? - Well, definitely practice, you know, figure out as quickly as you can
what type of, you know, business
area are you interested in? Because as a data analyst, I feel the business
knowledge is so relevant and not just the technical knowledge, and not just the, you know,
statistics, mathematics behind of it, but also how does it apply to a particular business environment? That's something that I
often see that's lacking from data analysts and they
know the theory very well,
but not necessarily how to apply for that particular business setting. So I think it's great to try and learn a little about a particular industry, about a particular business
to then see how to best apply
those data analytics practices to that specific area. And really, it really differs from one industry to another, from one company to another.
but, you know, to hone your skills too, there's so much great content out there that you could take that it's free. Of course, there's paid resources too, which kind of help you
go through a more
structured learning process. There's a lot of great, you
know, networking activities and a lot of great support and communities that you can go into. One that I would recommend
and I highly value is called
AI and ML by Tom Ives. And he has a great Slack networking group and they meet every week to basically go through a particular learning piece or just tackle a problem together,
and they're such a supportive group that it can just go and be
yourself, ask questions, don't be afraid to learn
and make mistakes with them and they'll help you along the way. And another resource that I recommend
is the dedicated circle, because very similarly, they are just an amazing community that put a lot of great content and there's even, you
know, job opportunities,
co-op opportunities that
are sometimes posted. And as I mentioned, a lot of great events
and informative sessions that you can learn from. - Excellent, and we can also link those
both in the description
of the episode also. So before we let you go, is there anything else that
you'd like our listeners to know that we didn't get a
chance to cover today? - It's something that I've
learned from Kate Stratchne
and that's to just do it, which I know is the Nike motto, but it really holds true because so far we're afraid
of taking that first step, we're afraid of failing that
we don't do anything about it
and we're maybe sometimes over planning, or we're thinking, okay
well, we're not ready yet, we're not good enough for this. So whatever you have in mind, whatever career path you wanna embark on,
whatever career step you wanna take, whatever lesson you wanna learn, whatever course you wanna go into and where, you know, should
I do it, should I not? I think, you know what, just do it,
listen to your instinct a little bit more and be a little bit more risk adverse while taking a calculated risk of course. - Yes, I love it. Just do it.
- Just do it.
- So, George, thank you so
much for joining us today. - My pleasure. Thank you so much for having me and thank you so much for
these lovely questions. - It was a pleasure.
And to all of our listeners, if you haven't already, check out our previous podcasts, they're available wherever
you listen to podcasts. For more information
about starting your career
as a data scientist, go to dataanalytics.buffalostate.edu and don't forget to subscribe so that you get a notification each time we release a new episode
of "Buffalo State Data Talk." (upbeat music)
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